ArXiv

DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification

2026-07-16 · 15:37 UTC ·Pengkun Wang, Weijia Cao, Ning Wang... ·1 min read
Authors
Pengkun Wang, Weijia Cao, Ning Wang...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.15128v1
PDF
https://arxiv.org/pdf/2607.15128v1

Brief

DAPGNet is a dynamic adaptive physics-guided graph diffusion network for hyperspectral image classification that injects contiguous-band physical priors into relation-level graph learning. It encodes multiscale spectral priors per node, constructs a prior-aware sparse topology, converts learned edges into attention biases, and uses a physics gate plus cross-scale fusion. On four benchmarks (Indian Pines, WHU-Hi-LongKou, Houston2013, Houston2018) it yields top OA/AA/Kappa, with AA gains of 3.64–7.31 points. The paper (ArXiv 2026-07-16) reports ablation and sensitivity studies validating each component.

Why it matters

DAPGNet achieves state-of-the-art OA, AA, and Kappa on Indian Pines, WHU-Hi-LongKou, Houston2013, and Houston2018; it improves average accuracy (AA) by 3.64–7.31 percentage points over the strongest competing method.

Key details

  • Architectural innovations include node-wise multiscale physical-prior encoding of contiguous spectral responses; a two-stage prior-aware sparse graph constructor combining spectral-spatial affinity, physical-prior consistency, and spatial distance; learned edge weights converted to additive attention biases; a physical gate for node-/feature-wise interpolation between graph-aggregated and projected physical-prior features; cross-scale fusion; and training with main classification, auxiliary supervision, and second-order spectral smoothness regularization.
Source evidence

Abstract

Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topology from spatial proximity, superpixel connectivity, or learned feature affinity. However, the spectral physical prior carried by contiguous bands has limited influence on topology estimation and message propagation. This paper presents DAPGNet, a dynamic adaptive physics-guided graph diffusion network that injects a structure-constrained physical prior into relation-level graph learning. DAPGNet first encodes contiguous spectral responses into node-wise multiscale physical-prior representations. A two-stage graph constructor then combines spectral-spatial affinity, physical-prior consistency, and spatial distance to form a physical-prior-aware sparse topology. During graph diffusion, learned edge weights are transformed into additive attention biases, while a physical gate performs node-wise and feature-wise interpolation between graph-aggregated features and projected physical-prior features. Cross-scale fusion integrates node states from different diffusion depths, and the network is optimized with main classification, auxiliary supervision, and second-order spectral smoothness regularization. Experiments on Indian Pines, WHU-Hi-LongKou, Houston2013, and Houston2018 show that DAPGNet achieves the best OA, AA, and Kappa among representative CNN-, Transformer-, Mamba-, and graph-based baselines. It improves AA over the strongest competing method by 3.64 to 7.31 percentage points across the four datasets. Ablation and sensitivity analyses further support the complementary effects of physical-prior extraction, prior-aware topology construction, physics-gated propagation, and spectral smoothness regularization.

Comment: 9 figures and 9 tables. Pengkun Wang and Weijia Cao contributed equally to this work